{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "44f4b286-2aae-41b5-8bb1-bc6ea37062c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "train_jpg = glob.glob('./农作物朝向检测挑战赛公开数据-初赛/train/*.jpg')\n",
    "train_jpg.sort()\n",
    "\n",
    "test_jpg = glob.glob('./农作物朝向检测挑战赛公开数据-初赛/test/*.jpg')\n",
    "test_jpg.sort()\n",
    "\n",
    "train_txt = glob.glob('./农作物朝向检测挑战赛公开数据-初赛/train/*.txt')\n",
    "train_txt.sort()\n",
    "train_txt = np.array([open(x).readlines() for x in train_txt]).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e688aa3c-3bf4-42af-a19b-3291689f584b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "# Define the transform\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),  # Resize the image to 224x224 pixels\n",
    "    transforms.ToTensor()  # Convert the image to a PyTorch tensor\n",
    "])\n",
    "\n",
    "model = torchvision.models.resnet18(weights='ResNet18_Weights.DEFAULT')\n",
    "model.fc = torch.nn.Identity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66108968-019d-451c-8559-8fa7ddf9695b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_feat = []\n",
    "for path in train_jpg:\n",
    "    img = Image.open(path)\n",
    "    img = transform(img)\n",
    "    with torch.no_grad():\n",
    "        feat = model(img[None, :, :, :])\n",
    "    train_feat.append(feat)\n",
    "    \n",
    "train_feat = torch.vstack(train_feat)\n",
    "train_feat = train_feat.data.numpy()\n",
    "\n",
    "\n",
    "test_feat = []\n",
    "for path in test_jpg:\n",
    "    img = Image.open(path)\n",
    "    img = transform(img)\n",
    "    with torch.no_grad():\n",
    "        feat = model(img[None, :, :, :])\n",
    "    test_feat.append(feat)\n",
    "    \n",
    "test_feat = torch.vstack(test_feat)\n",
    "test_feat = test_feat.data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e271e39-5d0b-4e25-a08a-34dc8e4b8bb5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "m = LinearRegression()\n",
    "m.fit(train_feat, train_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05c28168-0db5-42c2-abb3-7081052af558",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import shutil, os\n",
    "if os.path.exists('submit'):\n",
    "    shutil.rmtree('submit')\n",
    "    \n",
    "os.mkdir('submit')\n",
    "\n",
    "for path, feat in zip(test_jpg, m.predict(test_feat)):\n",
    "    up = open('./submit/' + os.path.basename(path)[:-4] + '.txt', 'w')\n",
    "    for f in feat:\n",
    "        up.write(str(f) + '\\n')\n",
    "    up.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de31f739-22e6-469b-84f2-815473098c31",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating: submit/ (stored 0%)\n",
      "updating: submit/00000195.txt (deflated 3%)\n",
      "updating: submit/00000192.txt (stored 0%)\n",
      "updating: submit/00000162.txt (stored 0%)\n",
      "updating: submit/00000165.txt (stored 0%)\n",
      "updating: submit/00000156.txt (stored 0%)\n",
      "updating: submit/00000151.txt (stored 0%)\n",
      "updating: submit/00000173.txt (stored 0%)\n",
      "updating: submit/00000170.txt (stored 0%)\n",
      "updating: submit/00000161.txt (stored 0%)\n",
      "updating: submit/00000168.txt (deflated 3%)\n",
      "updating: submit/00000159.txt (stored 0%)\n",
      "updating: submit/00000176.txt (deflated 3%)\n",
      "updating: submit/00000158.txt (stored 0%)\n",
      "updating: submit/00000183.txt (stored 0%)\n",
      "updating: submit/00000198.txt (stored 0%)\n",
      "updating: submit/00000197.txt (stored 0%)\n",
      "updating: submit/00000174.txt (stored 0%)\n",
      "updating: submit/00000155.txt (stored 0%)\n",
      "updating: submit/00000184.txt (stored 0%)\n",
      "updating: submit/00000186.txt (stored 0%)\n",
      "updating: submit/00000166.txt (stored 0%)\n",
      "updating: submit/00000169.txt (stored 0%)\n",
      "updating: submit/00000154.txt (stored 0%)\n",
      "updating: submit/00000199.txt (deflated 6%)\n",
      "updating: submit/00000178.txt (stored 0%)\n",
      "updating: submit/00000185.txt (deflated 3%)\n",
      "updating: submit/00000180.txt (deflated 6%)\n",
      "updating: submit/00000177.txt (deflated 3%)\n",
      "updating: submit/00000182.txt (deflated 3%)\n",
      "updating: submit/00000164.txt (stored 0%)\n",
      "updating: submit/00000189.txt (stored 0%)\n",
      "updating: submit/00000181.txt (stored 0%)\n",
      "updating: submit/00000153.txt (stored 0%)\n",
      "updating: submit/00000193.txt (deflated 13%)\n",
      "updating: submit/00000175.txt (stored 0%)\n",
      "updating: submit/00000152.txt (stored 0%)\n",
      "updating: submit/00000171.txt (stored 0%)\n",
      "updating: submit/00000187.txt (stored 0%)\n",
      "updating: submit/00000172.txt (stored 0%)\n",
      "updating: submit/00000167.txt (stored 0%)\n",
      "updating: submit/00000163.txt (stored 0%)\n",
      "updating: submit/00000188.txt (stored 0%)\n",
      "updating: submit/00000160.txt (deflated 6%)\n",
      "updating: submit/00000196.txt (stored 0%)\n",
      "updating: submit/00000194.txt (deflated 3%)\n",
      "updating: submit/00000191.txt (stored 0%)\n",
      "updating: submit/00000179.txt (deflated 6%)\n",
      "updating: submit/00000150.txt (stored 0%)\n",
      "updating: submit/00000190.txt (stored 0%)\n",
      "updating: submit/00000157.txt (stored 0%)\n"
     ]
    }
   ],
   "source": [
    "!zip -r submit.zip submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fc967df-ef40-4aa2-b53e-2fe7ff3c347f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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